A product retailer may need to make certain critical decisions in the lifetime of a particular commodity or product. At a certain period during sales activity, he may need to decide whether to mark down the selling price of an item, for example, or to re-buy or cancel other orders. He may wish to know how the numbers of markdowns can be kept to a minimum, or optimally targeted. For the products having good sales results, the retailer may wish to re-buy; for the products having poor sales results, the retailer may wish to cancel out. The decisions made will have a direct impact on realized profits and whether end-of-year targets are met.
If the retailer could better predict the impact of various pricing and promotional strategies, he could better negotiate with his suppliers. If the low-priced merchandise is selling out too early, for example, the selling price can be raised, but the amount should be such that there is no end-of-season over-stock problem.
Thus, there is a particular need for a system and method for more reliably producing such forecasts. Moreover, there is a need for an analytical method for investigating the impact of various re-buy, cancellation, re-price, promotion, and clearance markdown strategies.